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Fairness dilemmas arise when groups are faced with making decisions about how to share their resources, rewards, or payoffs. Since resources are limited, groups need to decide on fair ways of apportioning them out to their members. These fairness judgments are determined by procedural and distributive forms of social justice.
In its broadest sense, justice is the idea that individuals should be treated fairly. According to the Stanford Encyclopedia of Philosophy, the most plausible candidate for a core definition comes from the Institutes of Justinian, a codification of Roman Law from the sixth century AD, where justice is defined as "the constant and perpetual will to render to each his due".
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Envy-freeness, also known as no-envy, is a criterion for fair division.It says that, when resources are allocated among people with equal rights, each person should receive a share that is, in their eyes, at least as good as the share received by any other agent.
The Potter Box is a model for making ethical decisions, developed by Ralph B. Potter, Jr., professor of social ethics emeritus at Harvard Divinity School. [1] It is commonly used by communication ethics scholars.
God's justice is rooted in an unwavering fairness, devoid of favoritism. The epistle to the Colossians further reinforces the notion of impartiality, stating that those who commit wrongdoings will be held accountable for their actions without any partiality based on their personhood.
Principlism is an applied ethics approach to the examination of moral dilemmas centering the application of certain ethical principles. This approach to ethical decision-making has been prevalently adopted in various professional fields, largely because it sidesteps complex debates in moral philosophy at the theoretical level.
Fairness, absence of bias in specific realms: In American broadcasting, presentation of controversies in accord with the Fairness Doctrine. In computer science, fairness is a property of unbounded nondeterminism. In computer science, and specifically in machine learning, fairness is a desirable property of algorithms to avoid bias.